Abstract:
Index selection plays a substantial role in database performance by reducing the I/O cost. Existing index advisors apply different heuristic methods to search the large s...Show MoreMetadata
Abstract:
Index selection plays a substantial role in database performance by reducing the I/O cost. Existing index advisors apply different heuristic methods to search the large search space of possible attributes for indexing. These heuristic approaches do not have a mechanism to learn about the goodness of the recommended index set. Thus, they might choose the same index set with a low impact on I/O cost reduction. Learning from their decisions can improve the quality of the recommended index set. We believe that Deep Reinforcement Learning (DRL) is a solution to tackle this issue. Using DRL, an index advisor can improve its decision using the feedbacks of its decisions. In this paper, we propose a DRL-index advisor for a cluster database. We describe the major components such as agent, environment, set of actions, the reward function, and other modules. We conclude the paper with open challenges and possible future work.
Date of Conference: 20-24 April 2020
Date Added to IEEE Xplore: 15 May 2020
ISBN Information:
ISSN Information:
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- IEEE Keywords
- Index Terms
- Deep Learning ,
- Selectivity Index ,
- Deep Reinforcement Learning ,
- Index Set ,
- Open Challenges ,
- Heuristic Method ,
- Reward Function ,
- Heuristic Approach ,
- Deep Neural Network ,
- Forecasting ,
- Replica ,
- Processing Cost ,
- Fault-tolerant ,
- State Matrix ,
- Markov Decision Process ,
- Load Balancing ,
- Arrival Rate ,
- Learning Agent ,
- Routing Information ,
- Deep Q-network ,
- Deep Reinforcement Learning Framework ,
- Deep Q-learning ,
- Routing Table
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Deep Learning ,
- Selectivity Index ,
- Deep Reinforcement Learning ,
- Index Set ,
- Open Challenges ,
- Heuristic Method ,
- Reward Function ,
- Heuristic Approach ,
- Deep Neural Network ,
- Forecasting ,
- Replica ,
- Processing Cost ,
- Fault-tolerant ,
- State Matrix ,
- Markov Decision Process ,
- Load Balancing ,
- Arrival Rate ,
- Learning Agent ,
- Routing Information ,
- Deep Q-network ,
- Deep Reinforcement Learning Framework ,
- Deep Q-learning ,
- Routing Table
- Author Keywords